B.E. Electronics & Comm. Engg:Artificial Intelligence Techniques and Applications

Thapar University
In Patiala

Price on request
You can also call the Study Centre
17523... More
Compare this course with other similar courses
See all

Important information

  • Bachelor
  • Patiala
Description

Important information
Venues

Where and when

Starts Location
On request
Patiala
Thapar University P.O Box 32, 147004, Punjab, India
See map

Course programme

First Year: Semester I

Mathematics I
Engineering graphics
Computer Programming
Physics
Solid Mechanics
Communication Skills


First year: Semester II

Mathematics II
Manufacturing Process
Chemistry
Electrical and Electronic Science
Thermodynamics
Organizational Behavior


Second year: Semester I

Numerical and Statistical Methods
Measurement Science and Techniques
Electromagnetic Fields
Semiconductor Devices
Signals and Systems
Digital Electronic Circuits
Human Values, Ethics and IPR

Second year: Semester II

Optimization Techniques
Analog Electronic Circuits
Networks and Transmission Lines
Electrical Engineering Materials
Analog Communication Systems
Data Structure and Information Technology
Environmental Studies

Third year: Semester I

Digital Signal Processing for Communications Microprocessors
VLSI Circuit Design
Digital Communication Systems
Microelectronics Technology
Linear Integrated Circuits and Applications
Summer Training(6 weeks)


Third year: Semester II

Project Semester
Project
Industrial Training(6 weeks)


Fourth year: Semester I

Antenna and Wave Propagation
Modern Control Engineering
Wireless and Mobile Communication Systems
Microwave Engineering
Engineering Economics


Fourth year: Semester II

Optical Communication Systems
Advanced Communication Systems
HDL Based Digital Design
Total Quality Management
Minor Project

Artificial Intelligence Techniques and Application

Overview of Artificial Intelligence: The concept and importance of AI, fields related to AI human intelligence vs machine intelligence

Knowledge and general Concepts: General concept of knowledge, Acquisition, Knowledge Representation and organization: Prepositional and Predicate Logic, Theorem Proving, Structured Knowledge representation using Semantic Networks, Frames, Scripts,, Conceptual Graphs, Conceptual Dependencies, Knowledge Manipulation: Search space control, Uninformed search, Depth first search, Breadth first search, Depth first search with iterative deepening, Heuristic Search :Minimax Search procedure

Expert Systems: Expert systems: advantages, disadvantages, Expert system architecture, functions of various parts, Mechanism and role of inference engine, Types of Expert system, Tuning of expert systems, Role of Expert systems in instrumentation and process control

Overview of AI languages

Artificial Neural Networks: History of neural networks, Structure and function of a single neuron, biological neurons, artificial neuron models, Types of activation functions, Neural network architectures: Fully connected, layered, acyclic, feed forward, Neural learning : correlation, competitive, evaluation of networks; Supervised learning: Back propagation algorithm, Unsupervised learning, winner-take all networks, adaptive resonance theory, Application areas of neural networks : classification, clustering, pattern associations, function approximation, forecasting.

Fuzzy Logic: Fuzziness vs probability, Crisp logic vs fuzzy logic, Fuzzy sets and systems, operations on sets, fuzzy relations, membership functions, fuzzy rule generation, de fuzzification, Applications of Fuzzy Logic in process Control and motion control

Genetic Algorithms: introduction and concept, coding, reproduction, cross-over and mutation Scaling, fitness, applications.


Compare this course with other similar courses
See all